Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations2525
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory728.5 B

Variable types

Text3
Categorical8
Numeric9
DateTime1

Alerts

anioSalida is highly overall correlated with anioSemestreSalidaHigh correlation
anioSemestreSalida is highly overall correlated with anioSalida and 1 other fieldsHigh correlation
asesor is highly overall correlated with departamento_anonym and 2 other fieldsHigh correlation
departamento_anonym is highly overall correlated with asesor and 1 other fieldsHigh correlation
duracionCeba is highly overall correlated with edadFinalHigh correlation
edadFinal is highly overall correlated with duracionCebaHigh correlation
edadInicial is highly overall correlated with pesoInicialHigh correlation
fabricaAlimento is highly overall correlated with asesor and 1 other fieldsHigh correlation
numFinalAnimales is highly overall correlated with numInicialAnimalesHigh correlation
numInicialAnimales is highly overall correlated with numFinalAnimalesHigh correlation
pesoInicial is highly overall correlated with edadInicialHigh correlation
proveedorCorregido_anonym is highly overall correlated with asesorHigh correlation
semesreSalida is highly overall correlated with anioSemestreSalidaHigh correlation
genetica is highly imbalanced (66.1%) Imbalance
loteNo has unique values Unique
mortalidad has 311 (12.3%) zeros Zeros

Reproduction

Analysis started2025-01-03 02:01:46.358820
Analysis finished2025-01-03 02:02:01.172955
Duration14.81 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

loteNo
Text

Unique 

Distinct2525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
2025-01-02T21:02:01.432884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters60600
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2525 ?
Unique (%)100.0%

Sample

1st row5e4604c71c555bfa922bd644
2nd row5e4604c71c555bfa922bd68c
3rd row5e4604c71c555bfa922bd6b5
4th row5ea0d184cbe8d10cee382f9b
5th row5ef2216bde8636c49c3b8530
ValueCountFrequency (%)
5e4604c71c555bfa922bd644 1
 
< 0.1%
606cc612764f08b435411bb0 1
 
< 0.1%
5e4604c71c555bfa922bd6b5 1
 
< 0.1%
5ea0d184cbe8d10cee382f9b 1
 
< 0.1%
5ef2216bde8636c49c3b8530 1
 
< 0.1%
5ef2216bde8636c49c3b8539 1
 
< 0.1%
5ef2216bde8636c49c3b8538 1
 
< 0.1%
5f61495b47e46938e9de196e 1
 
< 0.1%
606cc612764f08b435411baf 1
 
< 0.1%
606cc612764f08b435411bbe 1
 
< 0.1%
Other values (2515) 2515
99.6%
2025-01-02T21:02:01.907403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 7024
 
11.6%
3 4923
 
8.1%
7 4469
 
7.4%
1 4398
 
7.3%
0 4028
 
6.6%
8 3898
 
6.4%
e 3756
 
6.2%
4 3731
 
6.2%
2 3182
 
5.3%
5 3142
 
5.2%
Other values (6) 18049
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 7024
 
11.6%
3 4923
 
8.1%
7 4469
 
7.4%
1 4398
 
7.3%
0 4028
 
6.6%
8 3898
 
6.4%
e 3756
 
6.2%
4 3731
 
6.2%
2 3182
 
5.3%
5 3142
 
5.2%
Other values (6) 18049
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 7024
 
11.6%
3 4923
 
8.1%
7 4469
 
7.4%
1 4398
 
7.3%
0 4028
 
6.6%
8 3898
 
6.4%
e 3756
 
6.2%
4 3731
 
6.2%
2 3182
 
5.3%
5 3142
 
5.2%
Other values (6) 18049
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 7024
 
11.6%
3 4923
 
8.1%
7 4469
 
7.4%
1 4398
 
7.3%
0 4028
 
6.6%
8 3898
 
6.4%
e 3756
 
6.2%
4 3731
 
6.2%
2 3182
 
5.3%
5 3142
 
5.2%
Other values (6) 18049
29.8%

departamento_anonym
Categorical

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size150.5 KiB
dp_3
1215 
dp_1
453 
dp_0
386 
dp_2
368 
dp_5
 
74
Other values (3)
 
29

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10100
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdp_0
2nd rowdp_0
3rd rowdp_0
4th rowdp_0
5th rowdp_0

Common Values

ValueCountFrequency (%)
dp_3 1215
48.1%
dp_1 453
 
17.9%
dp_0 386
 
15.3%
dp_2 368
 
14.6%
dp_5 74
 
2.9%
dp_6 11
 
0.4%
dp_7 10
 
0.4%
dp_4 8
 
0.3%

Length

2025-01-02T21:02:02.108397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:02.277257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
dp_3 1215
48.1%
dp_1 453
 
17.9%
dp_0 386
 
15.3%
dp_2 368
 
14.6%
dp_5 74
 
2.9%
dp_6 11
 
0.4%
dp_7 10
 
0.4%
dp_4 8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
d 2525
25.0%
p 2525
25.0%
_ 2525
25.0%
3 1215
12.0%
1 453
 
4.5%
0 386
 
3.8%
2 368
 
3.6%
5 74
 
0.7%
6 11
 
0.1%
7 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2525
25.0%
p 2525
25.0%
_ 2525
25.0%
3 1215
12.0%
1 453
 
4.5%
0 386
 
3.8%
2 368
 
3.6%
5 74
 
0.7%
6 11
 
0.1%
7 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2525
25.0%
p 2525
25.0%
_ 2525
25.0%
3 1215
12.0%
1 453
 
4.5%
0 386
 
3.8%
2 368
 
3.6%
5 74
 
0.7%
6 11
 
0.1%
7 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2525
25.0%
p 2525
25.0%
_ 2525
25.0%
3 1215
12.0%
1 453
 
4.5%
0 386
 
3.8%
2 368
 
3.6%
5 74
 
0.7%
6 11
 
0.1%
7 10
 
0.1%
Distinct77
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size152.6 KiB
2025-01-02T21:02:02.563594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8221782
Min length4

Characters and Unicode

Total characters12176
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowem_0
2nd rowem_0
3rd rowem_0
4th rowem_0
5th rowem_0
ValueCountFrequency (%)
em_46 250
 
9.9%
em_2 119
 
4.7%
em_17 117
 
4.6%
em_41 115
 
4.6%
em_52 112
 
4.4%
em_64 92
 
3.6%
em_0 85
 
3.4%
em_11 72
 
2.9%
em_7 51
 
2.0%
em_20 50
 
2.0%
Other values (67) 1462
57.9%
2025-01-02T21:02:03.040335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2525
20.7%
m 2525
20.7%
_ 2525
20.7%
4 895
 
7.4%
1 704
 
5.8%
6 661
 
5.4%
2 604
 
5.0%
5 455
 
3.7%
3 348
 
2.9%
7 289
 
2.4%
Other values (3) 645
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2525
20.7%
m 2525
20.7%
_ 2525
20.7%
4 895
 
7.4%
1 704
 
5.8%
6 661
 
5.4%
2 604
 
5.0%
5 455
 
3.7%
3 348
 
2.9%
7 289
 
2.4%
Other values (3) 645
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2525
20.7%
m 2525
20.7%
_ 2525
20.7%
4 895
 
7.4%
1 704
 
5.8%
6 661
 
5.4%
2 604
 
5.0%
5 455
 
3.7%
3 348
 
2.9%
7 289
 
2.4%
Other values (3) 645
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2525
20.7%
m 2525
20.7%
_ 2525
20.7%
4 895
 
7.4%
1 704
 
5.8%
6 661
 
5.4%
2 604
 
5.0%
5 455
 
3.7%
3 348
 
2.9%
7 289
 
2.4%
Other values (3) 645
 
5.3%
Distinct87
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size152.6 KiB
2025-01-02T21:02:03.339035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8253465
Min length4

Characters and Unicode

Total characters12184
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgr_0
2nd rowgr_0
3rd rowgr_0
4th rowgr_0
5th rowgr_0
ValueCountFrequency (%)
gr_48 250
 
9.9%
gr_2 119
 
4.7%
gr_18 117
 
4.6%
gr_70 92
 
3.6%
gr_0 85
 
3.4%
gr_9 81
 
3.2%
gr_72 73
 
2.9%
gr_12 72
 
2.9%
gr_43 52
 
2.1%
gr_8 51
 
2.0%
Other values (77) 1533
60.7%
2025-01-02T21:02:03.815538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 2525
20.7%
r 2525
20.7%
_ 2525
20.7%
4 697
 
5.7%
1 612
 
5.0%
2 610
 
5.0%
8 571
 
4.7%
7 472
 
3.9%
5 458
 
3.8%
3 399
 
3.3%
Other values (3) 790
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 2525
20.7%
r 2525
20.7%
_ 2525
20.7%
4 697
 
5.7%
1 612
 
5.0%
2 610
 
5.0%
8 571
 
4.7%
7 472
 
3.9%
5 458
 
3.8%
3 399
 
3.3%
Other values (3) 790
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 2525
20.7%
r 2525
20.7%
_ 2525
20.7%
4 697
 
5.7%
1 612
 
5.0%
2 610
 
5.0%
8 571
 
4.7%
7 472
 
3.9%
5 458
 
3.8%
3 399
 
3.3%
Other values (3) 790
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 2525
20.7%
r 2525
20.7%
_ 2525
20.7%
4 697
 
5.7%
1 612
 
5.0%
2 610
 
5.0%
8 571
 
4.7%
7 472
 
3.9%
5 458
 
3.8%
3 399
 
3.3%
Other values (3) 790
 
6.5%

proveedorCorregido_anonym
Categorical

High correlation 

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size150.6 KiB
pn_0
1100 
pn_6
997 
pn_7
 
95
pn_8
 
85
pn_1
 
83
Other values (6)
165 

Length

Max length5
Median length4
Mean length4.0142574
Min length4

Characters and Unicode

Total characters10136
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpn_0
2nd rowpn_0
3rd rowpn_0
4th rowpn_0
5th rowpn_0

Common Values

ValueCountFrequency (%)
pn_0 1100
43.6%
pn_6 997
39.5%
pn_7 95
 
3.8%
pn_8 85
 
3.4%
pn_1 83
 
3.3%
pn_4 59
 
2.3%
pn_10 36
 
1.4%
pn_2 28
 
1.1%
pn_3 18
 
0.7%
pn_5 17
 
0.7%

Length

2025-01-02T21:02:04.037180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pn_0 1100
43.6%
pn_6 997
39.5%
pn_7 95
 
3.8%
pn_8 85
 
3.4%
pn_1 83
 
3.3%
pn_4 59
 
2.3%
pn_10 36
 
1.4%
pn_2 28
 
1.1%
pn_3 18
 
0.7%
pn_5 17
 
0.7%

Most occurring characters

ValueCountFrequency (%)
p 2525
24.9%
n 2525
24.9%
_ 2525
24.9%
0 1136
11.2%
6 997
 
9.8%
1 119
 
1.2%
7 95
 
0.9%
8 85
 
0.8%
4 59
 
0.6%
2 28
 
0.3%
Other values (3) 42
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 2525
24.9%
n 2525
24.9%
_ 2525
24.9%
0 1136
11.2%
6 997
 
9.8%
1 119
 
1.2%
7 95
 
0.9%
8 85
 
0.8%
4 59
 
0.6%
2 28
 
0.3%
Other values (3) 42
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 2525
24.9%
n 2525
24.9%
_ 2525
24.9%
0 1136
11.2%
6 997
 
9.8%
1 119
 
1.2%
7 95
 
0.9%
8 85
 
0.8%
4 59
 
0.6%
2 28
 
0.3%
Other values (3) 42
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 2525
24.9%
n 2525
24.9%
_ 2525
24.9%
0 1136
11.2%
6 997
 
9.8%
1 119
 
1.2%
7 95
 
0.9%
8 85
 
0.8%
4 59
 
0.6%
2 28
 
0.3%
Other values (3) 42
 
0.4%

fabricaAlimento
Categorical

High correlation 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size150.5 KiB
pp_8
664 
pp_3
551 
pp_0
396 
pp_7
315 
pp_2
220 
Other values (4)
379 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10100
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpp_0
2nd rowpp_0
3rd rowpp_0
4th rowpp_0
5th rowpp_0

Common Values

ValueCountFrequency (%)
pp_8 664
26.3%
pp_3 551
21.8%
pp_0 396
15.7%
pp_7 315
12.5%
pp_2 220
 
8.7%
pp_6 159
 
6.3%
pp_1 138
 
5.5%
pp_5 74
 
2.9%
pp_4 8
 
0.3%

Length

2025-01-02T21:02:04.236585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:04.418609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pp_8 664
26.3%
pp_3 551
21.8%
pp_0 396
15.7%
pp_7 315
12.5%
pp_2 220
 
8.7%
pp_6 159
 
6.3%
pp_1 138
 
5.5%
pp_5 74
 
2.9%
pp_4 8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
p 5050
50.0%
_ 2525
25.0%
8 664
 
6.6%
3 551
 
5.5%
0 396
 
3.9%
7 315
 
3.1%
2 220
 
2.2%
6 159
 
1.6%
1 138
 
1.4%
5 74
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 5050
50.0%
_ 2525
25.0%
8 664
 
6.6%
3 551
 
5.5%
0 396
 
3.9%
7 315
 
3.1%
2 220
 
2.2%
6 159
 
1.6%
1 138
 
1.4%
5 74
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 5050
50.0%
_ 2525
25.0%
8 664
 
6.6%
3 551
 
5.5%
0 396
 
3.9%
7 315
 
3.1%
2 220
 
2.2%
6 159
 
1.6%
1 138
 
1.4%
5 74
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 5050
50.0%
_ 2525
25.0%
8 664
 
6.6%
3 551
 
5.5%
0 396
 
3.9%
7 315
 
3.1%
2 220
 
2.2%
6 159
 
1.6%
1 138
 
1.4%
5 74
 
0.7%

genetica
Categorical

Imbalance 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size150.5 KiB
lg_0
2114 
lg_1
230 
lg_3
 
64
lg_4
 
48
lg_2
 
39
Other values (2)
 
30

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10100
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlg_0
2nd rowlg_0
3rd rowlg_0
4th rowlg_0
5th rowlg_0

Common Values

ValueCountFrequency (%)
lg_0 2114
83.7%
lg_1 230
 
9.1%
lg_3 64
 
2.5%
lg_4 48
 
1.9%
lg_2 39
 
1.5%
lg_5 23
 
0.9%
lg_6 7
 
0.3%

Length

2025-01-02T21:02:04.640320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:04.830077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
lg_0 2114
83.7%
lg_1 230
 
9.1%
lg_3 64
 
2.5%
lg_4 48
 
1.9%
lg_2 39
 
1.5%
lg_5 23
 
0.9%
lg_6 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
l 2525
25.0%
g 2525
25.0%
_ 2525
25.0%
0 2114
20.9%
1 230
 
2.3%
3 64
 
0.6%
4 48
 
0.5%
2 39
 
0.4%
5 23
 
0.2%
6 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2525
25.0%
g 2525
25.0%
_ 2525
25.0%
0 2114
20.9%
1 230
 
2.3%
3 64
 
0.6%
4 48
 
0.5%
2 39
 
0.4%
5 23
 
0.2%
6 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2525
25.0%
g 2525
25.0%
_ 2525
25.0%
0 2114
20.9%
1 230
 
2.3%
3 64
 
0.6%
4 48
 
0.5%
2 39
 
0.4%
5 23
 
0.2%
6 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2525
25.0%
g 2525
25.0%
_ 2525
25.0%
0 2114
20.9%
1 230
 
2.3%
3 64
 
0.6%
4 48
 
0.5%
2 39
 
0.4%
5 23
 
0.2%
6 7
 
0.1%

duracionCeba
Real number (ℝ)

High correlation 

Distinct1025
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.353299
Minimum19.6
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:05.056056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum19.6
5-th percentile69.30666
Q177
median83.3131
Q391.1
95-th percentile103
Maximum144
Range124.4
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation10.956194
Coefficient of variation (CV)0.12988459
Kurtosis2.1958453
Mean84.353299
Median Absolute Deviation (MAD)7.0569
Skewness0.041730532
Sum212992.08
Variance120.03819
MonotonicityNot monotonic
2025-01-02T21:02:05.423128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 55
 
2.2%
81 51
 
2.0%
80 49
 
1.9%
79 42
 
1.7%
77 42
 
1.7%
74 34
 
1.3%
82 34
 
1.3%
76 33
 
1.3%
84 33
 
1.3%
85 32
 
1.3%
Other values (1015) 2120
84.0%
ValueCountFrequency (%)
19.6 1
< 0.1%
23.4 1
< 0.1%
25.4 1
< 0.1%
28.4 1
< 0.1%
32.6 1
< 0.1%
32.7 1
< 0.1%
35.1 1
< 0.1%
42.2 1
< 0.1%
42.6 1
< 0.1%
44.9 1
< 0.1%
ValueCountFrequency (%)
144 1
< 0.1%
123.9 1
< 0.1%
123 1
< 0.1%
120 2
0.1%
118.6862 1
< 0.1%
117.9 1
< 0.1%
117.8 1
< 0.1%
117 1
< 0.1%
116.7847 1
< 0.1%
116.4 1
< 0.1%

edadFinal
Real number (ℝ)

High correlation 

Distinct1063
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.15384
Minimum92.6
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:05.641171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum92.6
5-th percentile142
Q1151
median156.45
Q3163
95-th percentile174.866
Maximum206
Range113.4
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.14155
Coefficient of variation (CV)0.064532627
Kurtosis3.5319851
Mean157.15384
Median Absolute Deviation (MAD)5.79
Skewness-0.24239513
Sum396813.46
Variance102.85104
MonotonicityNot monotonic
2025-01-02T21:02:05.856955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155 50
 
2.0%
153 46
 
1.8%
152 41
 
1.6%
157 41
 
1.6%
154 40
 
1.6%
156 40
 
1.6%
158 40
 
1.6%
160 38
 
1.5%
159 36
 
1.4%
150 35
 
1.4%
Other values (1053) 2118
83.9%
ValueCountFrequency (%)
92.6 1
< 0.1%
92.9 1
< 0.1%
96.9 1
< 0.1%
98.7 1
< 0.1%
98.9 1
< 0.1%
101.7 1
< 0.1%
104 1
< 0.1%
109.3 1
< 0.1%
110.3 1
< 0.1%
113 1
< 0.1%
ValueCountFrequency (%)
206 1
< 0.1%
193.4 1
< 0.1%
193 1
< 0.1%
192 1
< 0.1%
190.2 1
< 0.1%
188.1 1
< 0.1%
188 1
< 0.1%
187.5 1
< 0.1%
187.2 1
< 0.1%
186.9 1
< 0.1%

edadInicial
Real number (ℝ)

High correlation 

Distinct738
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.800545
Minimum53
Maximum104.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:06.049043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile67
Q170
median71
Q374
95-th percentile84.8
Maximum104.82
Range51.82
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.6590165
Coefficient of variation (CV)0.077733162
Kurtosis4.1919428
Mean72.800545
Median Absolute Deviation (MAD)2
Skewness1.7576145
Sum183821.38
Variance32.024468
MonotonicityNot monotonic
2025-01-02T21:02:06.260944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 305
 
12.1%
69 119
 
4.7%
71 113
 
4.5%
72 78
 
3.1%
73 69
 
2.7%
79 46
 
1.8%
77 34
 
1.3%
67 34
 
1.3%
78 33
 
1.3%
68 33
 
1.3%
Other values (728) 1661
65.8%
ValueCountFrequency (%)
53 1
< 0.1%
56 1
< 0.1%
56.4088 1
< 0.1%
58.765 1
< 0.1%
60 1
< 0.1%
60.4567 1
< 0.1%
61 2
0.1%
61.2 1
< 0.1%
61.6 1
< 0.1%
61.9933 1
< 0.1%
ValueCountFrequency (%)
104.82 1
< 0.1%
100.2 1
< 0.1%
99.9 1
< 0.1%
98.9 1
< 0.1%
98.85 1
< 0.1%
98.5 2
0.1%
98.2 1
< 0.1%
98.03 1
< 0.1%
97.3 1
< 0.1%
97.14 1
< 0.1%

pesoInicial
Real number (ℝ)

High correlation 

Distinct2161
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.282284
Minimum20
Maximum56.0926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:06.484010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.912
Q128.15
median30.3862
Q333.4091
95-th percentile42.13628
Maximum56.0926
Range36.0926
Interquartile range (IQR)5.2591

Descriptive statistics

Standard deviation5.0967279
Coefficient of variation (CV)0.16292698
Kurtosis3.1327043
Mean31.282284
Median Absolute Deviation (MAD)2.5563
Skewness1.4001936
Sum78987.767
Variance25.976635
MonotonicityNot monotonic
2025-01-02T21:02:06.749843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 7
 
0.3%
29 7
 
0.3%
27 6
 
0.2%
29.2 6
 
0.2%
31.07 5
 
0.2%
33.5 5
 
0.2%
37 5
 
0.2%
30.1 5
 
0.2%
31 5
 
0.2%
27.3 4
 
0.2%
Other values (2151) 2470
97.8%
ValueCountFrequency (%)
20 1
< 0.1%
20.0191 1
< 0.1%
20.0459 2
0.1%
20.09 1
< 0.1%
20.1704 2
0.1%
20.5556 2
0.1%
20.6667 1
< 0.1%
21.0714 2
0.1%
21.18 1
< 0.1%
21.2411 1
< 0.1%
ValueCountFrequency (%)
56.0926 1
< 0.1%
54.9269 1
< 0.1%
54.9049 1
< 0.1%
53.9981 1
< 0.1%
53.9575 1
< 0.1%
53.7238 1
< 0.1%
53.5193 1
< 0.1%
52.9632 1
< 0.1%
52.7107 1
< 0.1%
52.4939 1
< 0.1%

pesoFinal
Real number (ℝ)

Distinct2360
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.90491
Minimum81.56
Maximum138.755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:06.992456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum81.56
5-th percentile96.6808
Q1107.5897
median114.8867
Q3120.89
95-th percentile128.69824
Maximum138.755
Range57.195
Interquartile range (IQR)13.3003

Descriptive statistics

Standard deviation9.8419211
Coefficient of variation (CV)0.086404712
Kurtosis0.0037144998
Mean113.90491
Median Absolute Deviation (MAD)6.6271
Skewness-0.35968582
Sum287609.9
Variance96.86341
MonotonicityNot monotonic
2025-01-02T21:02:07.257645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.35 4
 
0.2%
107.6209 3
 
0.1%
110.33 3
 
0.1%
117.09 3
 
0.1%
115 3
 
0.1%
120.22 3
 
0.1%
113.81 3
 
0.1%
121.22 3
 
0.1%
106.04 3
 
0.1%
116.83 3
 
0.1%
Other values (2350) 2494
98.8%
ValueCountFrequency (%)
81.56 1
< 0.1%
82.003 1
< 0.1%
82.0624 1
< 0.1%
82.56 1
< 0.1%
83.24 1
< 0.1%
84.2557 1
< 0.1%
84.51 1
< 0.1%
84.6 1
< 0.1%
85.11 1
< 0.1%
85.12 1
< 0.1%
ValueCountFrequency (%)
138.755 1
< 0.1%
138.5283 1
< 0.1%
138.5259 1
< 0.1%
138.2691 1
< 0.1%
137.7698 1
< 0.1%
137.7167 1
< 0.1%
137.6647 1
< 0.1%
137.4215 1
< 0.1%
137.376 1
< 0.1%
137.0045 1
< 0.1%
Distinct685
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Minimum2019-01-01 00:00:00
Maximum2023-12-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-02T21:02:07.512842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:02:07.755875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

anioSalida
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size150.5 KiB
2021
726 
2020
674 
2022
647 
2019
386 
2023
92 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10100
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 726
28.8%
2020 674
26.7%
2022 647
25.6%
2019 386
15.3%
2023 92
 
3.6%

Length

2025-01-02T21:02:07.990197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:08.167547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2021 726
28.8%
2020 674
26.7%
2022 647
25.6%
2019 386
15.3%
2023 92
 
3.6%

Most occurring characters

ValueCountFrequency (%)
2 5311
52.6%
0 3199
31.7%
1 1112
 
11.0%
9 386
 
3.8%
3 92
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5311
52.6%
0 3199
31.7%
1 1112
 
11.0%
9 386
 
3.8%
3 92
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5311
52.6%
0 3199
31.7%
1 1112
 
11.0%
9 386
 
3.8%
3 92
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5311
52.6%
0 3199
31.7%
1 1112
 
11.0%
9 386
 
3.8%
3 92
 
0.9%

semesreSalida
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size143.1 KiB
1
1331 
2
1194 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2525
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

Length

2025-01-02T21:02:08.342790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:08.491498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

Most occurring characters

ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1331
52.7%
2 1194
47.3%

anioSemestreSalida
Categorical

High correlation 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size157.9 KiB
2021-S1
375 
2021-S2
351 
2022-S1
348 
2020-S2
343 
2020-S1
331 
Other values (4)
777 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters17675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-S2
2nd row2019-S2
3rd row2019-S2
4th row2020-S1
5th row2020-S1

Common Values

ValueCountFrequency (%)
2021-S1 375
14.9%
2021-S2 351
13.9%
2022-S1 348
13.8%
2020-S2 343
13.6%
2020-S1 331
13.1%
2022-S2 299
11.8%
2019-S2 201
8.0%
2019-S1 185
7.3%
2023-S1 92
 
3.6%

Length

2025-01-02T21:02:08.661232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T21:02:08.827353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2021-s1 375
14.9%
2021-s2 351
13.9%
2022-s1 348
13.8%
2020-s2 343
13.6%
2020-s1 331
13.1%
2022-s2 299
11.8%
2019-s2 201
8.0%
2019-s1 185
7.3%
2023-s1 92
 
3.6%

Most occurring characters

ValueCountFrequency (%)
2 6505
36.8%
0 3199
18.1%
- 2525
 
14.3%
S 2525
 
14.3%
1 2443
 
13.8%
9 386
 
2.2%
3 92
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6505
36.8%
0 3199
18.1%
- 2525
 
14.3%
S 2525
 
14.3%
1 2443
 
13.8%
9 386
 
2.2%
3 92
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6505
36.8%
0 3199
18.1%
- 2525
 
14.3%
S 2525
 
14.3%
1 2443
 
13.8%
9 386
 
2.2%
3 92
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6505
36.8%
0 3199
18.1%
- 2525
 
14.3%
S 2525
 
14.3%
1 2443
 
13.8%
9 386
 
2.2%
3 92
 
0.5%

asesor
Categorical

High correlation 

Distinct26
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size151.9 KiB
rt_18
306 
rt_19
286 
rt_6
276 
rt_0
229 
rt_17
169 
Other values (21)
1259 

Length

Max length5
Median length5
Mean length4.550099
Min length4

Characters and Unicode

Total characters11489
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrt_0
2nd rowrt_0
3rd rowrt_0
4th rowrt_0
5th rowrt_0

Common Values

ValueCountFrequency (%)
rt_18 306
12.1%
rt_19 286
11.3%
rt_6 276
10.9%
rt_0 229
 
9.1%
rt_17 169
 
6.7%
rt_12 138
 
5.5%
rt_2 137
 
5.4%
rt_7 132
 
5.2%
rt_16 122
 
4.8%
rt_8 117
 
4.6%
Other values (16) 613
24.3%

Length

2025-01-02T21:02:09.085092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rt_18 306
12.1%
rt_19 286
11.3%
rt_6 276
10.9%
rt_0 229
 
9.1%
rt_17 169
 
6.7%
rt_12 138
 
5.5%
rt_2 137
 
5.4%
rt_7 132
 
5.2%
rt_16 122
 
4.8%
rt_8 117
 
4.6%
Other values (16) 613
24.3%

Most occurring characters

ValueCountFrequency (%)
r 2525
22.0%
t 2525
22.0%
_ 2525
22.0%
1 1459
12.7%
8 423
 
3.7%
6 398
 
3.5%
2 391
 
3.4%
0 321
 
2.8%
7 301
 
2.6%
9 294
 
2.6%
Other values (3) 327
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2525
22.0%
t 2525
22.0%
_ 2525
22.0%
1 1459
12.7%
8 423
 
3.7%
6 398
 
3.5%
2 391
 
3.4%
0 321
 
2.8%
7 301
 
2.6%
9 294
 
2.6%
Other values (3) 327
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2525
22.0%
t 2525
22.0%
_ 2525
22.0%
1 1459
12.7%
8 423
 
3.7%
6 398
 
3.5%
2 391
 
3.4%
0 321
 
2.8%
7 301
 
2.6%
9 294
 
2.6%
Other values (3) 327
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2525
22.0%
t 2525
22.0%
_ 2525
22.0%
1 1459
12.7%
8 423
 
3.7%
6 398
 
3.5%
2 391
 
3.4%
0 321
 
2.8%
7 301
 
2.6%
9 294
 
2.6%
Other values (3) 327
 
2.8%

numFinalAnimales
Real number (ℝ)

High correlation 

Distinct1130
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.58931
Minimum13
Maximum11030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:09.290777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile44
Q1163
median367
Q3764
95-th percentile1930.6
Maximum11030
Range11017
Interquartile range (IQR)601

Descriptive statistics

Standard deviation704.76558
Coefficient of variation (CV)1.1973809
Kurtosis52.550544
Mean588.58931
Median Absolute Deviation (MAD)243
Skewness5.0063934
Sum1486188
Variance496694.52
MonotonicityNot monotonic
2025-01-02T21:02:09.533425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 39
 
1.5%
44 32
 
1.3%
42 29
 
1.1%
45 21
 
0.8%
41 19
 
0.8%
43 17
 
0.7%
50 11
 
0.4%
146 10
 
0.4%
150 10
 
0.4%
47 10
 
0.4%
Other values (1120) 2327
92.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
21 2
0.1%
25 1
 
< 0.1%
27 4
0.2%
28 1
 
< 0.1%
30 4
0.2%
32 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 3
0.1%
ValueCountFrequency (%)
11030 1
< 0.1%
10298 1
< 0.1%
8824 1
< 0.1%
8395 1
< 0.1%
8240 1
< 0.1%
4539 1
< 0.1%
3849 1
< 0.1%
3567 1
< 0.1%
3538 1
< 0.1%
3441 1
< 0.1%

numInicialAnimales
Real number (ℝ)

High correlation 

Distinct1140
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.57347
Minimum13
Maximum11242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:09.771036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile44
Q1167
median375
Q3777
95-th percentile1997
Maximum11242
Range11229
Interquartile range (IQR)610

Descriptive statistics

Standard deviation720.83079
Coefficient of variation (CV)1.1982423
Kurtosis51.00139
Mean601.57347
Median Absolute Deviation (MAD)247
Skewness4.9318324
Sum1518973
Variance519597.03
MonotonicityNot monotonic
2025-01-02T21:02:10.151854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 48
 
1.9%
42 38
 
1.5%
44 29
 
1.1%
45 18
 
0.7%
43 18
 
0.7%
49 14
 
0.6%
40 12
 
0.5%
150 12
 
0.5%
39 10
 
0.4%
51 10
 
0.4%
Other values (1130) 2316
91.7%
ValueCountFrequency (%)
13 1
 
< 0.1%
21 2
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 4
0.2%
30 4
0.2%
32 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 3
0.1%
ValueCountFrequency (%)
11242 1
< 0.1%
10343 1
< 0.1%
8960 1
< 0.1%
8564 1
< 0.1%
8439 1
< 0.1%
4675 1
< 0.1%
3991 1
< 0.1%
3630 1
< 0.1%
3589 1
< 0.1%
3493 1
< 0.1%

mortalidad
Real number (ℝ)

Zeros 

Distinct536
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8803921
Minimum0
Maximum10
Zeros311
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:10.376422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.51
Q32.6
95-th percentile5.03
Maximum10
Range10
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation1.6330027
Coefficient of variation (CV)0.86843734
Kurtosis2.8754209
Mean1.8803921
Median Absolute Deviation (MAD)0.87
Skewness1.4782815
Sum4747.99
Variance2.6666978
MonotonicityNot monotonic
2025-01-02T21:02:10.640387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311
 
12.3%
2.17 19
 
0.8%
1.08 17
 
0.7%
2.38 16
 
0.6%
0.69 16
 
0.6%
1.96 15
 
0.6%
1.32 15
 
0.6%
0.97 14
 
0.6%
1.02 14
 
0.6%
0.72 13
 
0.5%
Other values (526) 2075
82.2%
ValueCountFrequency (%)
0 311
12.3%
0.1 2
 
0.1%
0.13 2
 
0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 2
 
0.1%
0.18 1
 
< 0.1%
0.19 1
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
ValueCountFrequency (%)
10 1
< 0.1%
9.89 1
< 0.1%
9.61 1
< 0.1%
9.48 1
< 0.1%
9.04 1
< 0.1%
8.99 1
< 0.1%
8.84 2
0.1%
8.83 1
< 0.1%
8.76 1
< 0.1%
8.63 1
< 0.1%

conversionAlimenticia
Real number (ℝ)

Distinct1692
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2366013
Minimum1.802
Maximum2.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2025-01-02T21:02:10.882707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.802
5-th percentile2.00218
Q12.1337
median2.2228
Q32.33
95-th percentile2.51072
Maximum2.79
Range0.988
Interquartile range (IQR)0.1963

Descriptive statistics

Standard deviation0.15379719
Coefficient of variation (CV)0.068763794
Kurtosis0.39199943
Mean2.2366013
Median Absolute Deviation (MAD)0.0978
Skewness0.37729751
Sum5647.4183
Variance0.023653576
MonotonicityNot monotonic
2025-01-02T21:02:11.098145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.19 20
 
0.8%
2.05 20
 
0.8%
2.13 16
 
0.6%
2.24 16
 
0.6%
2.25 15
 
0.6%
2.1 14
 
0.6%
2.18 14
 
0.6%
2.15 14
 
0.6%
2.08 14
 
0.6%
2.17 12
 
0.5%
Other values (1682) 2370
93.9%
ValueCountFrequency (%)
1.802 1
 
< 0.1%
1.82 3
0.1%
1.8254 1
 
< 0.1%
1.826 2
0.1%
1.83 1
 
< 0.1%
1.831 1
 
< 0.1%
1.832 1
 
< 0.1%
1.834 1
 
< 0.1%
1.837 1
 
< 0.1%
1.8376 1
 
< 0.1%
ValueCountFrequency (%)
2.79 1
< 0.1%
2.7607 1
< 0.1%
2.7589 1
< 0.1%
2.754 1
< 0.1%
2.7524 2
0.1%
2.7433 1
< 0.1%
2.743 1
< 0.1%
2.7366 1
< 0.1%
2.7196 1
< 0.1%
2.7149 2
0.1%

Interactions

2025-01-02T21:01:59.039577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:48.169514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:49.590930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:50.856612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:52.218294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:53.695735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:55.042535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:56.350807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:57.629363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:59.182522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:48.311528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:49.740023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:51.004517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:52.361015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:53.845245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:55.187279image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:56.508088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:57.784346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:59.336312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:48.494005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:49.875906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:51.154401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-02T21:01:51.295590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:52.772234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:54.133068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:55.464594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:56.786756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:58.050930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:59.651567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:48.864970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:50.150747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:51.456203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:52.927365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-02T21:01:59.807203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:49.021545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:50.296710image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:51.603657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-02T21:01:51.891975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:53.391536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-02T21:01:58.773808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:02:00.267341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:49.447110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:50.712706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:52.047921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:53.538694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:54.895255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:56.200050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:57.493532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-02T21:01:58.901506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-01-02T21:02:11.276375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
anioSalidaanioSemestreSalidaasesorconversionAlimenticiadepartamento_anonymduracionCebaedadFinaledadInicialfabricaAlimentogeneticamortalidadnumFinalAnimalesnumInicialAnimalespesoFinalpesoInicialproveedorCorregido_anonymsemesreSalida
anioSalida1.0000.9990.2620.0630.1630.1050.1000.1300.1370.1050.0870.0500.0510.1430.1350.1370.185
anioSemestreSalida0.9991.0000.1910.0480.1330.0830.0750.0950.1030.0920.0810.0400.0420.1200.1130.1070.999
asesor0.2620.1911.0000.1980.8710.3090.3290.2600.9970.4860.1750.2800.2790.2500.2740.5170.000
conversionAlimenticia0.0630.0480.1981.0000.1430.3360.4880.1060.1750.1290.3040.1960.2000.1420.0800.0840.037
departamento_anonym0.1630.1330.8710.1431.0000.1570.1830.1980.8460.2580.0690.1180.1190.2510.1570.2990.000
duracionCeba0.1050.0830.3090.3360.1571.0000.813-0.3770.2040.0960.2330.2360.2380.351-0.4590.0840.000
edadFinal0.1000.0750.3290.4880.1830.8131.0000.1070.2140.0980.2620.3600.3620.457-0.1070.0900.033
edadInicial0.1300.0950.2600.1060.198-0.3770.1071.0000.1870.175-0.0620.0680.0670.0950.6320.1620.039
fabricaAlimento0.1370.1030.9970.1750.8460.2040.2140.1871.0000.3510.1100.1410.1420.2190.1930.4450.000
genetica0.1050.0920.4860.1290.2580.0960.0980.1750.3511.0000.0370.0460.0470.1060.2140.3290.031
mortalidad0.0870.0810.1750.3040.0690.2330.262-0.0620.1100.0371.0000.2170.229-0.022-0.2360.0690.055
numFinalAnimales0.0500.0400.2800.1960.1180.2360.3600.0680.1410.0460.2171.0001.0000.198-0.0560.1820.000
numInicialAnimales0.0510.0420.2790.2000.1190.2380.3620.0670.1420.0470.2291.0001.0000.197-0.0590.1770.000
pesoFinal0.1430.1200.2500.1420.2510.3510.4570.0950.2190.106-0.0220.1980.1971.0000.3250.1440.096
pesoInicial0.1350.1130.2740.0800.157-0.459-0.1070.6320.1930.214-0.236-0.056-0.0590.3251.0000.1780.065
proveedorCorregido_anonym0.1370.1070.5170.0840.2990.0840.0900.1620.4450.3290.0690.1820.1770.1440.1781.0000.060
semesreSalida0.1850.9990.0000.0370.0000.0000.0330.0390.0000.0310.0550.0000.0000.0960.0650.0601.000

Missing values

2025-01-02T21:02:00.520404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T21:02:00.970887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loteNodepartamento_anonymempresa_anonymgranja_anonymproveedorCorregido_anonymfabricaAlimentogeneticaduracionCebaedadFinaledadInicialpesoInicialpesoFinalfechaSalidaanioSalidasemesreSalidaanioSemestreSalidaasesornumFinalAnimalesnumInicialAnimalesmortalidadconversionAlimenticia
05e4604c71c555bfa922bd644dp_0em_0gr_0pn_0pp_0lg_072.0152.080.030.9500103.56009/09/2019201922019-S2rt_02202262.652.1760
15e4604c71c555bfa922bd68cdp_0em_0gr_0pn_0pp_0lg_081.0154.073.026.6100100.640023/10/2019201922019-S2rt_01872016.972.2090
25e4604c71c555bfa922bd6b5dp_0em_0gr_0pn_0pp_0lg_079.0152.073.028.3200102.070019/11/2019201922019-S2rt_01942034.432.1350
35ea0d184cbe8d10cee382f9bdp_0em_0gr_0pn_0pp_0lg_076.0155.079.029.3400114.120013/03/2020202012020-S1rt_02032082.402.1050
45ef2216bde8636c49c3b8530dp_0em_0gr_0pn_0pp_0lg_074.0154.080.034.0400118.64001/04/2020202012020-S1rt_01851892.122.2220
55ef2216bde8636c49c3b8539dp_0em_0gr_0pn_0pp_0lg_074.0153.079.027.5900100.310013/05/2020202012020-S1rt_02022030.492.0490
65ef2216bde8636c49c3b8538dp_0em_0gr_0pn_0pp_0lg_074.0153.079.029.5700109.92006/05/2020202012020-S1rt_02142181.832.0660
75f61495b47e46938e9de196edp_0em_0gr_0pn_0pp_0lg_076.0156.080.026.9400109.520031/07/2020202022020-S2rt_02232261.331.9130
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